In the following subsection we evaluate the support recovery capabilities of RLS in different settings. We compare the performance of RLS to several strong baselines, such as: the LASSO [1]}, IRL1 [2]}, TL [3]}, OMP [4]}, ISD [5]} and RAWLS [6]}. We apply RLS with \(m=100\)  which was shown stable across different settings (as demonstrated in Fig. REF  smaller values of \(m\)  may also work in practice). We evaluate performance in terms of the empirical probability for perfect support recovery. This probability is estimated as the portion of simulations which obtained perfect support recovery out of 200 runs. Perfect support recovery is counted only if \(S({\mbox{$\theta $}})=S(\widehat{{\mbox{$\theta $}}})\) , where \(S({\mbox{$\theta $}}):= \lbrace i \in 1,...,D | {\mbox{$\theta $}}_i \ne 0 \rbrace \) .
<FIGURE>